A Computational Framework for Data Fusion in MEMS-Based Cardiac and Respiratory Gating

Abstract

Dual cardiac and respiratory gating is a well-known technique for motion compensation in nuclear medicine imaging. In this study, we present a new data fusion framework for dual cardiac and respiratory gating based on multidimensional microelectromechanical (MEMS) motion sensors. Our approach aims at robust estimation of the chest vibrations, that is, high-frequency precordial vibrations and low-frequency respiratory movements for prospective gating in positron emission tomography (PET), computed tomography (CT), and radiotherapy. Our sensing modality in the context of this paper is a single dual sensor unit, including accelerometer and gyroscope sensors to measure chest movements in three different orientations. Since accelerometer- and gyroscope-derived respiration signals represent the inclination of the chest, they are similar in morphology and have the same units. Therefore, we use principal component analysis (PCA) to combine them into a single signal. In contrast to this, the accelerometer- and gyroscope-derived cardiac signals correspond to the translational and rotational motions of the chest, and have different waveform characteristics and units. To combine these signals, we use independent component analysis (ICA) in order to obtain the underlying cardiac motion. From this cardiac motion signal, we obtain the systolic and diastolic phases of cardiac cycles by using an adaptive multi-scale peak detector and a short-time autocorrelation function. Three groups of subjects, including healthy controls (n = 7), healthy volunteers (n = 12), and patients with a history of coronary artery disease (n = 19) were studied to establish a quantitative framework for assessing the performance of the presented work in prospective imaging applications. The results of this investigation showed a fairly strong positive correlation (average r = 0.73 to 0.87) between the MEMS-derived (including corresponding PCA fusion) respiration curves and the reference optical camera and respiration belt sensors. Additionally, the mean time offset of MEMS-driven triggers from camera-driven triggers was 0.23 to 0.3 ± 0.15 to 0.17 s. For each cardiac cycle, the feature of the MEMS signals indicating a systolic time interval was identified, and its relation to the total cardiac cycle length was also reported. The findings of this study suggest that the combination of chest angular velocity and accelerations using ICA and PCA can help to develop a robust dual cardiac and respiratory gating solution using only MEMS sensors. Therefore, the methods presented in this paper should help improve predictions of the cardiac and respiratory quiescent phases, particularly with the clinical patients. This study lays the groundwork for future research into clinical PET/CT imaging based on dual inertial sensors.

Quantification of Cardiac Kinetic Energy and Its Changes During Transmural Myocardial Infarction Assessed by Multi-Dimensional Seismocardiography

“…However, whether non-invasive accelerometers and gyroscopes recording signals in multiple (linear and rotational) dimensions can be affected by hemodynamic changes during acute myocardial infarction and reperfusion is not known. Recently, non-invasive techniques based on microaccelerometers and gyroscopes exploring rotational velocities and accelerations produced by heart contraction have been introduced (15)(16)(17): rotational velocities measured using noninvasive tri-axial gyroscopes provide information on several mechanical events occurring during a contractile cycle as compared to echocardiography (16). The rotational kinetic energy obtained from tri-axial gyroscopes can accurately identify the first and the second peak of the SCG (15).…”

A Respiratory Motion Estimation Method Based on Inertial Measurement Units for Gated Positron Emission Tomography

“…The presented work is a continuation of our previous research [9,21,23]. In this work we present a new multi-sensor measurement system for estimating the sensors' positions, and thus, the polarity and magnitude of the respiratory motion, in order to perform amplitude-based gating in PET imaging.…”

“…Various sensor fusion methods are used to combine information from different types of sensors and different sensor nodes. Common sensor fusion methods include the principal component analysis (PCA) [17,21], ICA [7,21], Kalman filters [12,19], and a pose estimation method [22] developed specially to fuse signals obtained from IMU and MARG sensors [6,17]. Nearly all of these studies concentrate on estimating the 3D orientations of the sensor nodes.…”

“…In [23] this method was extended to use both MEMS accelerometers and gyroscopes, and as a clinical application, MEMS gating of cardiac PET for two atherosclerotic patients was demonstrated. We improved the methodology's robustness and accuracy further by applying PCA to respiration filtering and ICA to cardiac filtering in [21]. The usefulness of MEMS sensors in the gating of medical imaging is based on the sensitivity of the sensors, on their high sampling rate, and on the possibility to simultaneously measure motion from several locations on the body.…”

“…The resulting signals are fused in order to obtain a respiratory motion signal, which is compared against a ground truth signal obtained using an optical marker-based solution (real-time position management (RPM) by Varian Medical Systems, Inc., Palo Alto, CA, USA). In comparison, in our previous works [9,21,23], we considered only a single MEMS-based IMU for respiratory motion estimation. It should also be noted that the presented method is the first reported IMU-based method which can measure the magnitude and phase of the respiratory motion without any extra assumptions of the radius of motion.…”